Webinar: "Autonomous Impinging Jets Mixing for LNP Process Development" by BIZON Labs & KNAUER
A live webinar hosted by Life Science Connect, presented by KNAUER in partnership with BIZON Labs (a spin-off of an MIT research project)
Lipid nanoparticles are now central to the delivery of genetic medicines, yet LNP process development remains empirical and resource-intensive. Identifying process parameters for scalable, reproducible, and robust manufacturing is challenging, as particle quality attributes are highly sensitive to formulation and processing conditions.
This webinar explores how impinging jets mixing can be integrated with automation, inline analytics, and data-driven optimization to accelerate LNP process development. Attendees will see how automated design of experiments, dynamic process sweeps, self-optimization, and predictive modeling can map complex process-property relationships and rapidly identify conditions that achieve target quality attributes.
We'll show how closed-loop experimentation can transform LNP development from an empirical workflow into a faster, more resource-sparing process, and discuss the challenges that remain on the path to fully autonomous nanoparticle manufacturing.
What you'll learn:
- How impinging jets mixing parameters influence LNP critical quality attributes such as size, structure, and morphology
- How automation enables faster, data-rich LNP process development
- How design of experiments, dynamic sweeps, self-optimization, and data-driven modeling support predictive and scalable LNP manufacturing
Who should attend:
- Formulation scientists working on RNA, mRNA, siRNA, or gene therapy delivery
- Process development scientists and engineers in nanoparticle manufacturing
- CMC, MSAT, and manufacturing scientists supporting LNP scale-up
- R&D leaders evaluating automated or data-driven approaches to drug delivery development
Speakers:
Peter Sagmeister, PhD, BIZON Labs (spinning out of MIT)
Peter Sagmeister earned his BSc and MSc from the University of Graz, Austria, and completed his PhD there in 2022 under Prof. Oliver Kappe, focusing on the digitalization of continuous flow chemistry for APIs and intermediates. During his postdoctoral work at the University of Graz and RCPE GmbH, he published extensively on process analytical technology, data science, and machine learning for small molecule process development. He is currently a Postdoctoral Associate in the Department of Chemical Engineering at MIT, where his work focuses on modernizing pharmaceutical manufacturing. Peter has collaborated with leading pharmaceutical companies, including Roche, Boehringer Ingelheim, Takeda, and AstraZeneca, to develop manufacturing platforms and data-driven workflows spanning small molecules to mRNA therapeutics.
Dr. Nadia Elghobashi-Meinhardt, KNAUER
Nadia Elghobashi-Meinhardt received a BS in Chemistry from Stanford University in 1998 and a PhD in Chemistry in 2005 from the Freie Universität Berlin. After completing postdoctoral work in computational biophysics at the Fritz-Haber-Institut Berlin, Heidelberg University, and the Technical University Berlin, Nadia joined the School of Chemistry at University College Dublin (Ireland) as Assistant Professor in 2023. At KNAUER, Nadia serves as Principal Consultant and Technical GTM in the Customized Solutions team supporting LNP projects.
Register for free via Life Science Connect: